{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 灰度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(414, 500)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2 #opencv读取的格式是BGR\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt#Matplotlib是RGB\n",
    "%matplotlib inline \n",
    "\n",
    "img=cv2.imread('cat.jpg')\n",
    "img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "img_gray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow(\"img_gray\", img_gray)\n",
    "cv2.waitKey(0)    \n",
    "cv2.destroyAllWindows() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HSV\n",
    "- H - 色调（主波长）。 \n",
    "- S - 饱和度（纯度/颜色的阴影）。 \n",
    "- V值（强度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n",
    "\n",
    "cv2.imshow(\"hsv\", hsv)\n",
    "cv2.waitKey(0)    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像阈值\n",
    "\n",
    "#### ret, dst = cv2.threshold(src, thresh, maxval, type)\n",
    "\n",
    "- src： 输入图，只能输入单通道图像，通常来说为灰度图\n",
    "- dst： 输出图\n",
    "- thresh： 阈值\n",
    "- maxval： 当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值\n",
    "- type：二值化操作的类型，包含以下5种类型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV\n",
    "\n",
    "- cv2.THRESH_BINARY           超过阈值部分取maxval（最大值），否则取0\n",
    "- cv2.THRESH_BINARY_INV    THRESH_BINARY的反转\n",
    "- cv2.THRESH_TRUNC            大于阈值部分设为阈值，否则不变\n",
    "- cv2.THRESH_TOZERO          大于阈值部分不改变，否则设为0\n",
    "- cv2.THRESH_TOZERO_INV  THRESH_TOZERO的反转\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)\n",
    "ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)\n",
    "ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)\n",
    "ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)\n",
    "\n",
    "titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']\n",
    "images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]\n",
    "\n",
    "for i in range(6):\n",
    "    plt.subplot(2, 3, i + 1), plt.imshow(images[i], 'gray')\n",
    "    plt.title(titles[i])\n",
    "    plt.xticks([]), plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像平滑"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('lenaNoise.png')\n",
    "\n",
    "cv2.imshow('img', img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 均值滤波\n",
    "# 简单的平均卷积操作\n",
    "blur = cv2.blur(img, (3, 3))\n",
    "\n",
    "cv2.imshow('blur', blur)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 方框滤波\n",
    "# 基本和均值一样，可以选择归一化\n",
    "box = cv2.boxFilter(img,-1,(3,3), normalize=True)  \n",
    "\n",
    "cv2.imshow('box', box)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 方框滤波\n",
    "# 基本和均值一样，可以选择归一化,容易越界\n",
    "box = cv2.boxFilter(img,-1,(3,3), normalize=False)  \n",
    "\n",
    "cv2.imshow('box', box)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 高斯滤波\n",
    "# 高斯模糊的卷积核里的数值是满足高斯分布，相当于更重视中间的\n",
    "aussian = cv2.GaussianBlur(img, (5, 5), 1)  \n",
    "\n",
    "cv2.imshow('aussian', aussian)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 中值滤波\n",
    "# 相当于用中值代替\n",
    "median = cv2.medianBlur(img, 5)  # 中值滤波\n",
    "\n",
    "cv2.imshow('median', median)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 展示所有的\n",
    "res = np.hstack((blur,aussian,median))\n",
    "#print (res)\n",
    "cv2.imshow('median vs average', res)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 形态学-腐蚀操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pie = cv2.imread('pie.png')\n",
    "\n",
    "cv2.imshow('pie', pie)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = np.ones((30,30),np.uint8) \n",
    "erosion_1 = cv2.erode(pie,kernel,iterations = 1)\n",
    "erosion_2 = cv2.erode(pie,kernel,iterations = 2)\n",
    "erosion_3 = cv2.erode(pie,kernel,iterations = 3)\n",
    "res = np.hstack((erosion_1,erosion_2,erosion_3))\n",
    "cv2.imshow('res', res)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 形态学-膨胀操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pie = cv2.imread('pie.png')\n",
    "\n",
    "kernel = np.ones((30,30),np.uint8) \n",
    "dilate_1 = cv2.dilate(pie,kernel,iterations = 1)\n",
    "dilate_2 = cv2.dilate(pie,kernel,iterations = 2)\n",
    "dilate_3 = cv2.dilate(pie,kernel,iterations = 3)\n",
    "res = np.hstack((dilate_1,dilate_2,dilate_3))\n",
    "cv2.imshow('res', res)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 开运算与闭运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开：先腐蚀，再膨胀\n",
    "img = cv2.imread('dige.png')\n",
    "\n",
    "kernel = np.ones((5,5),np.uint8) \n",
    "opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)\n",
    "\n",
    "cv2.imshow('opening', opening)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 闭：先膨胀，再腐蚀\n",
    "img = cv2.imread('dige.png')\n",
    "\n",
    "kernel = np.ones((5,5),np.uint8) \n",
    "closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)\n",
    "\n",
    "cv2.imshow('closing', closing)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 梯度运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 梯度=膨胀-腐蚀\n",
    "pie = cv2.imread('pie.png')\n",
    "kernel = np.ones((7,7),np.uint8) \n",
    "dilate = cv2.dilate(pie,kernel,iterations = 5)\n",
    "erosion = cv2.erode(pie,kernel,iterations = 5)\n",
    "\n",
    "res = np.hstack((dilate,erosion))\n",
    "\n",
    "cv2.imshow('res', res)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel)\n",
    "\n",
    "cv2.imshow('gradient', gradient)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 礼帽与黑帽\n",
    "- 礼帽 = 原始输入-开运算结果\n",
    "- 黑帽 = 闭运算-原始输入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#礼帽\n",
    "img = cv2.imread('dige.png')\n",
    "tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)\n",
    "cv2.imshow('tophat', tophat)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#黑帽\n",
    "img = cv2.imread('dige.png')\n",
    "blackhat  = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT, kernel)\n",
    "cv2.imshow('blackhat ', blackhat )\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像梯度-Sobel算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](sobel_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE)\n",
    "cv2.imshow(\"img\",img)\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dst = cv2.Sobel(src, ddepth, dx, dy, ksize)\n",
    "- ddepth:图像的深度\n",
    "- dx和dy分别表示水平和竖直方向\n",
    "- ksize是Sobel算子的大小\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_show(img,name):\n",
    "    cv2.imshow(name,img)\n",
    "    cv2.waitKey()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)\n",
    "\n",
    "cv_show(sobelx,'sobelx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "白到黑是正数，黑到白就是负数了，所有的负数会被截断成0，所以要取绝对值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)\n",
    "sobelx = cv2.convertScaleAbs(sobelx)\n",
    "cv_show(sobelx,'sobelx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)\n",
    "sobely = cv2.convertScaleAbs(sobely)  \n",
    "cv_show(sobely,'sobely')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分别计算x和y，再求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)\n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不建议直接计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)\n",
    "sobelxy = cv2.convertScaleAbs(sobelxy) \n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)\n",
    "sobelx = cv2.convertScaleAbs(sobelx)\n",
    "sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)\n",
    "sobely = cv2.convertScaleAbs(sobely)\n",
    "sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)\n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)\n",
    "sobelxy = cv2.convertScaleAbs(sobelxy) \n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像梯度-Scharr算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](scharr.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像梯度-laplacian算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](l.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#不同算子的差异\n",
    "img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)\n",
    "sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)\n",
    "sobelx = cv2.convertScaleAbs(sobelx)   \n",
    "sobely = cv2.convertScaleAbs(sobely)  \n",
    "sobelxy =  cv2.addWeighted(sobelx,0.5,sobely,0.5,0)  \n",
    "\n",
    "scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)\n",
    "scharry = cv2.Scharr(img,cv2.CV_64F,0,1)\n",
    "scharrx = cv2.convertScaleAbs(scharrx)   \n",
    "scharry = cv2.convertScaleAbs(scharry)  \n",
    "scharrxy =  cv2.addWeighted(scharrx,0.5,scharry,0.5,0) \n",
    "\n",
    "laplacian = cv2.Laplacian(img,cv2.CV_64F)\n",
    "laplacian = cv2.convertScaleAbs(laplacian)   \n",
    "\n",
    "res = np.hstack((sobelxy,scharrxy,laplacian))\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Canny边缘检测\n",
    "- 1)        使用高斯滤波器，以平滑图像，滤除噪声。\n",
    "\n",
    "- 2)        计算图像中每个像素点的梯度强度和方向。\n",
    "\n",
    "- 3)        应用非极大值（Non-Maximum Suppression）抑制，以消除边缘检测带来的杂散响应。\n",
    "\n",
    "- 4)        应用双阈值（Double-Threshold）检测来确定真实的和潜在的边缘。\n",
    "\n",
    "- 5)        通过抑制孤立的弱边缘最终完成边缘检测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1:高斯滤波器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](canny_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2:梯度和方向"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](canny_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3：非极大值抑制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](canny_3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](canny_6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4：双阈值检测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](canny_5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "img=cv2.imread(\"lena.jpg\",cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "v1=cv2.Canny(img,80,150)\n",
    "v2=cv2.Canny(img,50,100)\n",
    "\n",
    "res = np.hstack((v1,v2))\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "img=cv2.imread(\"car.png\",cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "v1=cv2.Canny(img,120,250)\n",
    "v2=cv2.Canny(img,50,100)\n",
    "\n",
    "res = np.hstack((v1,v2))\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像金字塔\n",
    "- 高斯金字塔\n",
    "- 拉普拉斯金字塔"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Pyramid_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 高斯金字塔：向下采样方法（缩小）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Pyramid_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 高斯金字塔：向上采样方法（放大）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Pyramid_3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 340, 3)\n"
     ]
    }
   ],
   "source": [
    "img=cv2.imread(\"AM.png\")\n",
    "cv_show(img,'img')\n",
    "print (img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(884, 680, 3)\n"
     ]
    }
   ],
   "source": [
    "up=cv2.pyrUp(img)\n",
    "cv_show(up,'up')\n",
    "print (up.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(221, 170, 3)\n"
     ]
    }
   ],
   "source": [
    "down=cv2.pyrDown(img)\n",
    "cv_show(down,'down')\n",
    "print (down.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1768, 1360, 3)\n"
     ]
    }
   ],
   "source": [
    "up2=cv2.pyrUp(up)\n",
    "cv_show(up2,'up2')\n",
    "print (up2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "up=cv2.pyrUp(img)\n",
    "up_down=cv2.pyrDown(up)\n",
    "cv_show(up_down,'up_down')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_show(np.hstack((img,up_down)),'up_down')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "up=cv2.pyrUp(img)\n",
    "up_down=cv2.pyrDown(up)\n",
    "cv_show(img-up_down,'img-up_down')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 拉普拉斯金字塔"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Pyramid_4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "down=cv2.pyrDown(img)\n",
    "down_up=cv2.pyrUp(down)\n",
    "l_1=img-down_up\n",
    "cv_show(l_1,'l_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像轮廓"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cv2.findContours(img,mode,method)\n",
    "mode:轮廓检索模式\n",
    "- RETR_EXTERNAL ：只检索最外面的轮廓；\n",
    "- RETR_LIST：检索所有的轮廓，并将其保存到一条链表当中；\n",
    "- RETR_CCOMP：检索所有的轮廓，并将他们组织为两层：顶层是各部分的外部边界，第二层是空洞的边界;\n",
    "- RETR_TREE：检索所有的轮廓，并重构嵌套轮廓的整个层次;\n",
    "\n",
    "method:轮廓逼近方法\n",
    "- CHAIN_APPROX_NONE：以Freeman链码的方式输出轮廓，所有其他方法输出多边形（顶点的序列）。\n",
    "- CHAIN_APPROX_SIMPLE:压缩水平的、垂直的和斜的部分，也就是，函数只保留他们的终点部分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](chain.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了更高的准确率，使用二值图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('contours.png')\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)\n",
    "cv_show(thresh,'thresh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制轮廓"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#传入绘制图像，轮廓，轮廓索引，颜色模式，线条厚度\n",
    "# 注意需要copy,要不原图会变。。。\n",
    "draw_img = img.copy()\n",
    "res = cv2.drawContours(draw_img, contours, -1, (0, 0, 255), 2)\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_img = img.copy()\n",
    "res = cv2.drawContours(draw_img, contours, 0, (0, 0, 255), 2)\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 轮廓特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt = contours[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8500.5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#面积\n",
    "cv2.contourArea(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "437.9482651948929"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#周长，True表示闭合的\n",
    "cv2.arcLength(cnt,True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 轮廓近似"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](contours3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('contours2.png')\n",
    "\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n",
    "cnt = contours[0]\n",
    "\n",
    "draw_img = img.copy()\n",
    "res = cv2.drawContours(draw_img, [cnt], -1, (0, 0, 255), 2)\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 0.15*cv2.arcLength(cnt,True) \n",
    "approx = cv2.approxPolyDP(cnt,epsilon,True)\n",
    "\n",
    "draw_img = img.copy()\n",
    "res = cv2.drawContours(draw_img, [approx], -1, (0, 0, 255), 2)\n",
    "cv_show(res,'res')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "边界矩形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('contours.png')\n",
    "\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n",
    "cnt = contours[0]\n",
    "\n",
    "x,y,w,h = cv2.boundingRect(cnt)\n",
    "img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)\n",
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "轮廓面积与边界矩形比 0.5154317244724715\n"
     ]
    }
   ],
   "source": [
    "area = cv2.contourArea(cnt)\n",
    "x, y, w, h = cv2.boundingRect(cnt)\n",
    "rect_area = w * h\n",
    "extent = float(area) / rect_area\n",
    "print ('轮廓面积与边界矩形比',extent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "外接圆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x,y),radius = cv2.minEnclosingCircle(cnt) \n",
    "center = (int(x),int(y)) \n",
    "radius = int(radius) \n",
    "img = cv2.circle(img,center,radius,(0,255,0),2)\n",
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 傅里叶变换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们生活在时间的世界中，早上7:00起来吃早饭，8:00去挤地铁，9:00开始上班。。。以时间为参照就是时域分析。\n",
    "\n",
    "但是在频域中一切都是静止的！\n",
    "\n",
    "https://zhuanlan.zhihu.com/p/19763358\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 傅里叶变换的作用\n",
    "\n",
    "- 高频：变化剧烈的灰度分量，例如边界\n",
    "\n",
    "- 低频：变化缓慢的灰度分量，例如一片大海\n",
    "\n",
    "### 滤波\n",
    "\n",
    "- 低通滤波器：只保留低频，会使得图像模糊\n",
    "\n",
    "- 高通滤波器：只保留高频，会使得图像细节增强\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- opencv中主要就是cv2.dft()和cv2.idft()，输入图像需要先转换成np.float32 格式。\n",
    "- 得到的结果中频率为0的部分会在左上角，通常要转换到中心位置，可以通过shift变换来实现。\n",
    "- cv2.dft()返回的结果是双通道的（实部，虚部），通常还需要转换成图像格式才能展示（0,255）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "img = cv2.imread('lena.jpg',0)\n",
    "\n",
    "img_float32 = np.float32(img)\n",
    "\n",
    "dft = cv2.dft(img_float32, flags = cv2.DFT_COMPLEX_OUTPUT)\n",
    "dft_shift = np.fft.fftshift(dft)\n",
    "# 得到灰度图能表示的形式\n",
    "magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))\n",
    "\n",
    "plt.subplot(121),plt.imshow(img, cmap = 'gray')\n",
    "plt.title('Input Image'), plt.xticks([]), plt.yticks([])\n",
    "plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')\n",
    "plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
